Top Modules Exploratory Analysis for All Lobes but Occipital
#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}Load preprocessed dataset (preprocessing code in 20_03_03_data_preprocessing.Rmd) and clustering (pipeline in 20_03_03_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org')
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybrid'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds)Relation to external clinical traits
Quantifying module-trait associations
Using the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
datTraits = datMeta %>% dplyr::select(Diagnosis, Brain_lobe, Sex, Age, PMI, RNAExtractionBatch) %>%
dplyr::rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}## [1] "1 correlation(s) could not be calculated"rm(ME_object)Note: The correlations between Modules and Diagonsis that cannot be calculated, weirdly enough, is because the initial correlation is too high, so it would be a very bad thing to lose these modules because of this numerical error. I’m going to fill in the values using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.
# Calculate the correlation tha failed with hetcor()
missing_modules = rownames(moduleTraitCor)[is.na(moduleTraitCor[,1])]
for(m in missing_modules){
cat(paste0('Correcting Module-Diagnosis correlation for Module ', m))
moduleTraitCor[m,'Diagnosis'] = polyserial(MEs[,m], datTraits$Diagnosis)
}## Correcting Module-Diagnosis correlation for Module ME#D39200## Warning in polyserial(MEs[, m], datTraits$Diagnosis): initial correlation
## inadmissible, -1.02954073623291, set to -0.9999rm(missing_modules)I’m going to select all the modules that have an absolute correlation higher than 0.9 with Diagnosis to study them
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitPvalue, datTraits, textMatrix, diagnosis_cor)Studying the modules with the highest absolute correlation to Diagnosis
top_modules = gsub('ME','',rownames(moduleTraitCor)[abs(moduleTraitCor[,'Diagnosis'])>0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))## Top modules selected: #D39200There’s only one module with a correlation higher than 0.9, so I’m going to include the largest positive correlation module, which has a correlation of 0.88
top_modules = gsub('ME','',rownames(moduleTraitCor)[moduleTraitCor[,'Diagnosis']>0.88 |
moduleTraitCor[,'Diagnosis']< -0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))## Top modules selected: #00BC5A, #D39200The modules consist mainly of points with very high (absolute) values in PC2 (which we know is related to lfc), so this result is consistent with the high correlation between Module and Diagnosis, although some of the points with the highest PC2 values do not belong to these top modules
The module with the positive correlation to Diagnosis is very big
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')'))
table(plot_data$ImportantModules)##
## #00BC5A #D39200 Others
## 2278 367 13654ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
ggtitle('Modules with strongest relation to Diagnosis'))rm(pca)Module Membership vs Gene Significance
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module),1],2),')')))
return(p)
}
create_plot(top_modules[1])create_plot(top_modules[2])rm(create_plot)SFARI Genes
List of top SFARI Genes in top modules ordered by SFARI score and Gene Significance
table_data = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, GS, gene.score, Module) %>% arrange(gene.score, desc(abs(GS))) %>%
dplyr::rename('Ensembl ID'=ID, 'Gene Symbol'=external_gene_id,
'SFARI score'=gene.score, 'Gene Significance'=GS)
kable(table_data %>% filter(Module == top_modules[1] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[1]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000049618 | ARID1B | 0.3366021 | 1 |
| ENSG00000108510 | MED13 | 0.8290707 | 2 |
| ENSG00000114166 | KAT2B | 0.8283772 | 2 |
| ENSG00000147050 | KDM6A | 0.7012818 | 2 |
| ENSG00000038382 | TRIO | 0.6534757 | 2 |
| ENSG00000257923 | CUX1 | 0.6211401 | 2 |
| ENSG00000112851 | ERBB2IP | 0.6039454 | 2 |
| ENSG00000123066 | MED13L | 0.5682006 | 2 |
| ENSG00000117139 | KDM5B | 0.5254979 | 2 |
| ENSG00000100354 | TNRC6B | 0.5219282 | 2 |
| ENSG00000165186 | PTCHD1 | 0.2358762 | 2 |
| ENSG00000215301 | DDX3X | 0.1783716 | 2 |
| ENSG00000181722 | ZBTB20 | 0.9181273 | 3 |
| ENSG00000141646 | SMAD4 | 0.8727621 | 3 |
| ENSG00000181090 | EHMT1 | 0.8445343 | 3 |
| ENSG00000116117 | PARD3B | 0.8355293 | 3 |
| ENSG00000168769 | TET2 | 0.8186246 | 3 |
| ENSG00000112655 | PTK7 | 0.7449802 | 3 |
| ENSG00000083168 | KAT6A | 0.7156954 | 3 |
| ENSG00000259207 | ITGB3 | 0.6981423 | 3 |
| ENSG00000079482 | OPHN1 | 0.6888942 | 3 |
| ENSG00000146247 | PHIP | 0.6580814 | 3 |
| ENSG00000197724 | PHF2 | 0.6446488 | 3 |
| ENSG00000008083 | JARID2 | 0.6262088 | 3 |
| ENSG00000117362 | APH1A | 0.5953940 | 3 |
| ENSG00000124126 | PREX1 | 0.4966418 | 3 |
| ENSG00000148737 | TCF7L2 | 0.4528527 | 3 |
| ENSG00000132510 | KDM6B | 0.4443304 | 3 |
| ENSG00000196628 | TCF4 | 0.4160231 | 3 |
| ENSG00000206190 | ATP10A | 0.4044905 | 3 |
| ENSG00000113742 | CPEB4 | 0.3986469 | 3 |
| ENSG00000050344 | NFE2L3 | 0.3515540 | 3 |
| ENSG00000180914 | OXTR | 0.3269342 | 3 |
| ENSG00000196092 | PAX5 | 0.2918344 | 3 |
| ENSG00000165995 | CACNB2 | 0.2850210 | 3 |
| ENSG00000204406 | MBD5 | 0.2243562 | 3 |
| ENSG00000166148 | AVPR1A | 0.1968984 | 3 |
| ENSG00000140557 | ST8SIA2 | -0.0619562 | 3 |
kable(table_data %>% filter(Module == top_modules[2] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[2]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000186487 | MYT1L | -0.1986426 | 1 |
| ENSG00000144285 | SCN1A | -0.8655277 | 3 |
| ENSG00000197535 | MYO5A | -0.8028405 | 3 |
| ENSG00000104388 | RAB2A | -0.7831104 | 3 |
| ENSG00000005955 | GGNBP2 | -0.7580022 | 3 |
| ENSG00000170745 | KCNS3 | -0.5624743 | 3 |
| ENSG00000138411 | HECW2 | -0.2744052 | 3 |
| ENSG00000168116 | KIAA1586 | -0.2680971 | 3 |
Modules with the strongest module-diagnosis correlation should have the highest percentage of SFARI Genes, but this doesn’t seem to be the case
plot_data = dataset %>% mutate('hasSFARIscore' = gene.score!='None') %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(p=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(p=round(p/n*100,2))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(this_row$hasSFARIscore==FALSE & this_row$p==100){
new_row = this_row
new_row$hasSFARIscore = TRUE
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
plot_data = plot_data %>% filter(hasSFARIscore==TRUE)
ggplotly(plot_data %>% ggplot(aes(MTcor, p, size=n)) + geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) + geom_hline(yintercept=mean(plot_data$p), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))rm(i, this_row, new_row, plot_data)Breaking the SFARI genes by score
scores = c(1,2,3,4,5,6,'None')
plot_data = dataset %>% group_by(Module, MTcor, gene.score) %>% summarise(n=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(N=n()), by='Module') %>%
mutate(p=round(n/N*100,2), gene.score = as.character(gene.score))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(sum(plot_data$Module == this_row$Module)<7){
missing_scores = which(! scores %in% plot_data$gene.score[plot_data$Module == this_row$Module])
for(s in missing_scores){
new_row = this_row
new_row$gene.score = as.character(s)
new_row$n = 0
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
}
plot_data = plot_data %>% filter(gene.score != 'None')
plot_function = function(i){
i = 2*i-1
plot_list = list()
for(j in 1:2){
plot_list[[j]] = ggplotly(plot_data %>% filter(gene.score==scores[i+j-1]) %>% ggplot(aes(MTcor, p, size=n)) +
geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module[plot_data$gene.score==scores[i+j-1]], alpha=0.5, aes(id=Module)) +
geom_hline(yintercept=mean(plot_data$p[plot_data$gene.score==scores[i+j-1]]), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))
}
p = subplot(plot_list, nrows=1) %>% layout(annotations = list(
list(x = 0.2 , y = 1.05, text = paste0('SFARI score ', scores[i]), showarrow = F, xref='paper', yref='paper'),
list(x = 0.8 , y = 1.05, text = paste0('SFARI score ', scores[i+1]), showarrow = F, xref='paper', yref='paper')))
return(p)
}
plot_function(1)plot_function(2)plot_function(3)rm(i, s, this_row, new_row, plot_function)Module Eigengenes
Since these modules have the strongest relation to autism, this pattern should be reflected in their model eigengenes, having two different behaviours for the samples corresponding to autism and the ones corresponding to control.
In both cases, the Eigengenes separate the behaviour between autism and control samples very clearly!
plot_EGs = function(module){
plot_data = data.frame('ID' = rownames(MEs), 'MEs' = MEs[,paste0('ME',module)], 'Diagnosis' = datMeta$Diagnosis)
p = plot_data %>% ggplot(aes(Diagnosis, MEs, fill=Diagnosis)) + geom_boxplot() + theme_minimal() + theme(legend.position='none') +
ggtitle(paste0('Module ', module, ' (MTcor=',round(moduleTraitCor[paste0('ME',module),1],2),')'))
return(p)
}
p1 = plot_EGs(top_modules[1])
p2 = plot_EGs(top_modules[2])
grid.arrange(p1, p2, nrow=1)
rm(plot_EGs, p1, p2)Identifying important genes
Selecting the modules with the highest correlation to Diagnosis, and, from them, the genes with the highest module membership-(absolute) gene significance
*Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t many SFARI genes in the top genes of each module, and all of the have a score of 5
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(importance = (MM+abs(GS))/2) %>% arrange(by=-importance) %>% top_n(20)
return(top_genes)
}
top_genes = list()
for(i in 1:length(top_modules)) top_genes[[i]] = create_table(top_modules[i])
kable(top_genes[[1]], caption=paste0('Top 10 genes for module ', top_modules[1], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[1]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000143384 | MCL1 | 0.9427142 | 0.8959477 | None | 0.9193309 |
| ENSG00000150457 | LATS2 | 0.9151257 | 0.9094471 | None | 0.9122864 |
| ENSG00000147065 | MSN | 0.9106850 | 0.9114844 | 5 | 0.9110847 |
| ENSG00000150907 | FOXO1 | 0.8681347 | 0.9245993 | None | 0.8963670 |
| ENSG00000116044 | NFE2L2 | 0.9017548 | 0.8708091 | None | 0.8862819 |
| ENSG00000148841 | ITPRIP | 0.8551664 | 0.9156699 | None | 0.8854182 |
| ENSG00000151491 | EPS8 | 0.8581006 | 0.9116522 | 5 | 0.8848764 |
| ENSG00000003402 | CFLAR | 0.8686402 | 0.8943214 | None | 0.8814808 |
| ENSG00000089159 | PXN | 0.8589460 | 0.9022448 | None | 0.8805954 |
| ENSG00000183864 | TOB2 | 0.8394053 | 0.9181283 | None | 0.8787668 |
| ENSG00000120063 | GNA13 | 0.8268945 | 0.9302263 | None | 0.8785604 |
| ENSG00000120690 | ELF1 | 0.8924432 | 0.8619871 | None | 0.8772152 |
| ENSG00000158615 | PPP1R15B | 0.8619953 | 0.8878566 | None | 0.8749259 |
| ENSG00000173530 | TNFRSF10D | 0.8455473 | 0.8979312 | None | 0.8717392 |
| ENSG00000084093 | REST | 0.8197831 | 0.9207914 | None | 0.8702873 |
| ENSG00000163629 | PTPN13 | 0.8638131 | 0.8668572 | None | 0.8653351 |
| ENSG00000102699 | PARP4 | 0.8332139 | 0.8920497 | None | 0.8626318 |
| ENSG00000161638 | ITGA5 | 0.8253777 | 0.8967534 | None | 0.8610655 |
| ENSG00000101871 | MID1 | 0.7762770 | 0.9457893 | None | 0.8610332 |
| ENSG00000137693 | YAP1 | 0.8661341 | 0.8529069 | None | 0.8595205 |
kable(top_genes[[2]], caption=paste0('Top 10 genes for module ', top_modules[2], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[2]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000165169 | DYNLT3 | 0.9076883 | -0.9200616 | None | 0.9138749 |
| ENSG00000123091 | RNF11 | 0.9275239 | -0.8986339 | None | 0.9130789 |
| ENSG00000197860 | SGTB | 0.8767247 | -0.9302899 | None | 0.9035073 |
| ENSG00000204116 | CHIC1 | 0.9068275 | -0.8992826 | None | 0.9030551 |
| ENSG00000169139 | UBE2V2 | 0.9091857 | -0.8846635 | None | 0.8969246 |
| ENSG00000184203 | PPP1R2 | 0.8535499 | -0.9071902 | None | 0.8803701 |
| ENSG00000213585 | VDAC1 | 0.8737078 | -0.8863331 | None | 0.8800204 |
| ENSG00000175582 | RAB6A | 0.8306959 | -0.9243819 | None | 0.8775389 |
| ENSG00000163577 | EIF5A2 | 0.8412486 | -0.9069291 | None | 0.8740888 |
| ENSG00000040341 | STAU2 | 0.8918535 | -0.8541028 | None | 0.8729781 |
| ENSG00000197885 | NKIRAS1 | 0.8607468 | -0.8810389 | None | 0.8708928 |
| ENSG00000175395 | ZNF25 | 0.8274191 | -0.9043655 | None | 0.8658923 |
| ENSG00000124785 | NRN1 | 0.8288069 | -0.9013313 | None | 0.8650691 |
| ENSG00000075303 | SLC25A40 | 0.8386439 | -0.8856682 | None | 0.8621560 |
| ENSG00000108946 | PRKAR1A | 0.8467692 | -0.8765254 | None | 0.8616473 |
| ENSG00000213424 | KRT222 | 0.8437953 | -0.8731470 | None | 0.8584711 |
| ENSG00000197170 | PSMD12 | 0.7878272 | -0.9257621 | None | 0.8567946 |
| ENSG00000150768 | DLAT | 0.7872120 | -0.9244614 | None | 0.8558367 |
| ENSG00000188730 | VWC2 | 0.8284389 | -0.8662499 | None | 0.8473444 |
| ENSG00000085788 | DDHD2 | 0.8011298 | -0.8926130 | None | 0.8468714 |
rm(create_table)pca = datExpr %>% prcomp
ids = c()
for(tg in top_genes) ids = c(ids, tg$ID)
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules &
ID %in% ids, 1, 0.1))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
theme_minimal() + ggtitle('Important genes identified through WGCNA')
Level of expression by Diagnosis for top genes, ordered by importance (defined above)
create_plot = function(i){
plot_data = datExpr[rownames(datExpr) %in% top_genes[[i]]$ID,] %>% mutate('ID' = rownames(.)) %>%
melt(id.vars='ID') %>% mutate(variable = gsub('X','',variable)) %>%
left_join(top_genes[[i]], by='ID') %>%
left_join(datMeta %>% dplyr::select(Dissected_Sample_ID, Diagnosis),
by = c('variable'='Dissected_Sample_ID')) %>% arrange(desc(importance))
p = ggplotly(plot_data %>% mutate(external_gene_id=factor(external_gene_id,
levels=unique(plot_data$external_gene_id), ordered=T)) %>%
ggplot(aes(external_gene_id, value, fill=Diagnosis)) + geom_boxplot() + theme_minimal() +
xlab(paste0('Top genes for module ', top_modules[i], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[i]][1],2), ')')) + ylab('Level of Expression') +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
return(p)
}
create_plot(1)create_plot(2)rm(create_plot)Enrichment Analysis
Using the package anRichment
It was designed by Peter Langfelder explicitly to perform enrichmen analysis on WGCNA’s modules in brain-related experiments (mainly Huntington’s Disease)
It has packages with brain annotations:
BrainDiseaseCollection: A Brain Disease Gene Set Collection for anRichment
MillerAIBSCollection: (included in anRichment) Contains gene sets collected by Jeremy A. Miller at AIBS of various cell type and brain region marker sets, gene sets collected from expression studies of developing brain, as well as a collection of transcription factor (TF) targets from the original ChEA study
The tutorial says it’s an experimental package
It’s not on CRAN nor in Bioconductor
# Prepare dataset
# Create dataset with top modules membership and removing the genes without an assigned module
EA_dataset = data.frame('ensembl_gene_id' = genes_info$ID,
module = ifelse(genes_info$Module %in% top_modules, genes_info$Module, 'other')) %>%
filter(genes_info$Module!='gray')
# Assign Entrez Gene Id to each gene
getinfo = c('ensembl_gene_id','entrezgene')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
biomart_output = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=EA_dataset$ensembl_gene_id, mart=mart)## Cache foundEA_dataset = EA_dataset %>% left_join(biomart_output, by='ensembl_gene_id')
for(tm in top_modules){
cat(paste0('\n',sum(EA_dataset$module==tm & is.na(EA_dataset$entrezgene)), ' genes from top module ',
tm, ' don\'t have an Entrez Gene ID'))
}##
## 36 genes from top module #00BC5A don't have an Entrez Gene ID
## 6 genes from top module #D39200 don't have an Entrez Gene IDrm(getinfo, mart, biomart_output, tm)# Manual: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/GeneAnnotation/Tutorials/anRichment-Tutorial1.pdf
collectGarbage()
# Prepare datasets
GO_col = buildGOcollection(organism = 'human', verbose = 0)## Loading required package: org.Hs.eg.db## ## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columnsinternal_col = internalCollection(organism = 'human')
MillerAIBS_col = MillerAIBSCollection(organism = 'human')
BrainDisease_col = BrainDiseaseCollection(organism = 'human')
combined_col = mergeCollections(GO_col, internal_col, MillerAIBS_col, BrainDisease_col)
# Print collections used
cat('Using collections: ')## Using collections:knownGroups(combined_col, sortBy = 'size')## [1] "GO"
## [2] "GO.BP"
## [3] "GO.MF"
## [4] "GO.CC"
## [5] "JA Miller at AIBS"
## [6] "Chip-X enrichment analysis (ChEA)"
## [7] "Brain"
## [8] "JAM"
## [9] "Prenatal brain"
## [10] "Brain region markers"
## [11] "Cortex"
## [12] "Brain region marker enriched gene sets"
## [13] "WGCNA"
## [14] "BrainRegionMarkers"
## [15] "BrainRegionMarkers.HBA"
## [16] "BrainRegionMarkers.HBA.localMarker(top200)"
## [17] "Postnatal brain"
## [18] "ImmunePathways"
## [19] "Markers of cortex layers"
## [20] "BrainLists"
## [21] "Cell type markers"
## [22] "Germinal brain"
## [23] "BrainRegionMarkers.HBA.globalMarker(top200)"
## [24] "Accelerated evolution"
## [25] "Postmitotic brain"
## [26] "BrainLists.Blalock_AD"
## [27] "BrainLists.DiseaseGenes"
## [28] "BloodAtlases"
## [29] "Verge Disease Genes"
## [30] "BloodAtlases.Whitney"
## [31] "BrainLists.JAXdiseaseGene"
## [32] "BrainLists.MO"
## [33] "Age-associated genes"
## [34] "BrainLists.Lu_Aging"
## [35] "Cell type marker enriched gene sets"
## [36] "BrainLists.CA1vsCA3"
## [37] "BrainLists.MitochondrialType"
## [38] "BrainLists.MO.2+_26Mar08"
## [39] "BrainLists.MO.Sugino"
## [40] "BloodAtlases.Gnatenko2"
## [41] "BloodAtlases.Kabanova"
## [42] "BrainLists.Voineagu"
## [43] "StemCellLists"
## [44] "StemCellLists.Lee"# Perform Enrichment Analysis
enrichment = enrichmentAnalysis(classLabels = EA_dataset$module, identifiers = EA_dataset$entrezgene,
refCollection = combined_col, #useBackground = 'given',
threshold = 1e-4, thresholdType = 'Bonferroni',
getOverlapEntrez = FALSE, getOverlapSymbols = TRUE)## enrichmentAnalysis: preparing data..
## ..working on label set 1 ..Results
kable(enrichment$enrichmentTable %>% filter(class==top_modules[1]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[1], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[1]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000143 | Lowest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 1.912662 | 2234 | 1450 | 386 |
| JAMiller.AIBS.000009 | VZ markers at 15-16 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Germinal brain | 0.00e+00 | 0e+00 | 1.931293 | 2234 | 1250 | 336 |
| JAM:003025 | Red Nucleus_IN_Mesencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.550663 | 2234 | 172 | 85 |
| GO:0002376 | immune system process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.533411 | 2234 | 2399 | 512 |
| GO:0007166 | cell surface receptor signaling pathway | GO|GO.BP | 0.00e+00 | 0e+00 | 1.511710 | 2234 | 2538 | 534 |
| GO:0007165 | signal transduction | GO|GO.BP | 0.00e+00 | 0e+00 | 1.322756 | 2234 | 4894 | 901 |
| JAM:002861 | Globus Pallidus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.524653 | 2234 | 159 | 78 |
| JAMiller.AIBS.000148 | Highest in VZ of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 0.00e+00 | 0e+00 | 2.069497 | 2234 | 677 | 195 |
| GO:0007154 | cell communication | GO|GO.BP | 0.00e+00 | 0e+00 | 1.288971 | 2234 | 5340 | 958 |
| GO:0023052 | signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.289038 | 2234 | 5323 | 955 |
| GO:0050896 | response to stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.213397 | 2234 | 7372 | 1245 |
| JAMiller.AIBS.000098 | Cortical oligodendrocytes (Olig2) | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 0.00e+00 | 0e+00 | 1.500797 | 2234 | 2274 | 475 |
| JAMiller.AIBS.000204 | RegionalWGCNA midfetal M34 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.00e+00 | 0e+00 | 2.321003 | 2234 | 421 | 136 |
| GO:0048513 | animal organ development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.419661 | 2234 | 2905 | 574 |
| GO:0070887 | cellular response to chemical stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.426692 | 2234 | 2795 | 555 |
| GO:0005886 | plasma membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 1.313559 | 2234 | 4343 | 794 |
| GO:0051716 | cellular response to stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.238449 | 2234 | 6167 | 1063 |
| GO:0071944 | cell periphery | GO|GO.CC | 0.00e+00 | 0e+00 | 1.307368 | 2234 | 4446 | 809 |
| GO:0009605 | response to external stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.519661 | 2234 | 1929 | 408 |
| GO:0032502 | developmental process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.262170 | 2234 | 5294 | 930 |
| JAM:003019 | Putamen_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.143381 | 2234 | 160 | 70 |
| GO:0048583 | regulation of response to stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.342098 | 2234 | 3635 | 679 |
| JAMiller.AIBS.000097 | Cortical astrocytes | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 0.00e+00 | 0e+00 | 1.457574 | 2234 | 2302 | 467 |
| GO:0022610 | biological adhesion | GO|GO.BP | 0.00e+00 | 0e+00 | 1.656290 | 2234 | 1258 | 290 |
| GO:0071310 | cellular response to organic substance | GO|GO.BP | 0.00e+00 | 0e+00 | 1.454967 | 2234 | 2316 | 469 |
| GO:0048856 | anatomical structure development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.271492 | 2234 | 4967 | 879 |
| JAM:003092 | upAging_oligo | JAM|BrainLists|BrainLists.Lu_Aging | 0.00e+00 | 0e+00 | 2.076236 | 2234 | 526 | 152 |
| GO:0006955 | immune response | GO|GO.BP | 0.00e+00 | 0e+00 | 1.566221 | 2234 | 1601 | 349 |
| GO:0007155 | cell adhesion | GO|GO.BP | 0.00e+00 | 0e+00 | 1.651431 | 2234 | 1253 | 288 |
| GO:0048518 | positive regulation of biological process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.257044 | 2234 | 5247 | 918 |
| GO:0042221 | response to chemical | GO|GO.BP | 0.00e+00 | 0e+00 | 1.325881 | 2234 | 3777 | 697 |
| GO:0009888 | tissue development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.552644 | 2234 | 1615 | 349 |
| GO:0030154 | cell differentiation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.342091 | 2234 | 3453 | 645 |
| GO:0048869 | cellular developmental process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.330092 | 2234 | 3630 | 672 |
| GO:0010033 | response to organic substance | GO|GO.BP | 0.00e+00 | 0e+00 | 1.389635 | 2234 | 2823 | 546 |
| GO:0001775 | cell activation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.662767 | 2234 | 1171 | 271 |
| JAMiller.AIBS.000136 | Layer6 enriched in adult macaque cortex | JA Miller at AIBS|Brain|Postnatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 2.715020 | 2234 | 217 | 82 |
| GO:0048731 | system development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.300740 | 2234 | 4082 | 739 |
| GO:0032501 | multicellular organismal process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.221238 | 2234 | 6048 | 1028 |
| JAMiller.AIBS.000434 | Genes bound by RELA in HUMAN FIBROSARCOMA from PMID 24523406 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.690558 | 2234 | 1054 | 248 |
| GO:0009966 | regulation of signal transduction | GO|GO.BP | 0.00e+00 | 0e+00 | 1.379703 | 2234 | 2760 | 530 |
| GO:0008283 | cell proliferation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.516515 | 2234 | 1644 | 347 |
| GO:0048522 | positive regulation of cellular process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.262935 | 2234 | 4665 | 820 |
| GO:0035295 | tube development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.728209 | 2234 | 898 | 216 |
| GO:0048585 | negative regulation of response to stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.549872 | 2234 | 1451 | 313 |
| JAMiller.AIBS.000084 | Bergman glia | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 0.00e+00 | 0e+00 | 1.605754 | 2234 | 1226 | 274 |
| JAMiller.AIBS.000062 | CortexWGCNA 15-21 post-conception weeks C36 SZ/VZenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 2.928864 | 2234 | 157 | 64 |
| GO:0009653 | anatomical structure morphogenesis | GO|GO.BP | 0.00e+00 | 0e+00 | 1.409650 | 2234 | 2314 | 454 |
| GO:0051239 | regulation of multicellular organismal process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.365073 | 2234 | 2758 | 524 |
| GO:0009986 | cell surface | GO|GO.CC | 0.00e+00 | 0e+00 | 1.830406 | 2234 | 683 | 174 |
| JAMiller.AIBS.000085 | Cerebellar astrocytes | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 0.00e+00 | 0e+00 | 1.539260 | 2234 | 1447 | 310 |
| GO:0045321 | leukocyte activation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.646389 | 2234 | 1043 | 239 |
| GO:0042981 | regulation of apoptotic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.561692 | 2234 | 1325 | 288 |
| GO:0042127 | regulation of cell proliferation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.551637 | 2234 | 1366 | 295 |
| GO:0007275 | multicellular organism development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.256250 | 2234 | 4564 | 798 |
| GO:0006954 | inflammatory response | GO|GO.BP | 0.00e+00 | 0e+00 | 1.887551 | 2234 | 590 | 155 |
| GO:0010941 | regulation of cell death | GO|GO.BP | 0.00e+00 | 0e+00 | 1.530065 | 2234 | 1451 | 309 |
| GO:0002252 | immune effector process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.663097 | 2234 | 985 | 228 |
| GO:0023051 | regulation of signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.328506 | 2234 | 3153 | 583 |
| GO:0016477 | cell migration | GO|GO.BP | 0.00e+00 | 0e+00 | 1.574213 | 2234 | 1246 | 273 |
| JAMiller.AIBS.000099 | Cortical oligodendrocytes (Cmtm5) | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 0.00e+00 | 0e+00 | 1.669136 | 2234 | 947 | 220 |
| GO:0043067 | regulation of programmed cell death | GO|GO.BP | 0.00e+00 | 0e+00 | 1.547263 | 2234 | 1342 | 289 |
| GO:0050793 | regulation of developmental process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.398545 | 2234 | 2281 | 444 |
| JAMiller.AIBS.000537 | Genes bound by TP63 in HUMAN EP156T from PMID 23658742 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.348757 | 2234 | 2818 | 529 |
| JAM:002965 | nucleus subceruleus_IN_Pontine Tegmentum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 2.893236 | 2234 | 149 | 60 |
| GO:0010646 | regulation of cell communication | GO|GO.BP | 0.00e+00 | 0e+00 | 1.325477 | 2234 | 3106 | 573 |
| GO:0098609 | cell-cell adhesion | GO|GO.BP | 0.00e+00 | 0e+00 | 1.764534 | 2234 | 737 | 181 |
| GO:0034097 | response to cytokine | GO|GO.BP | 0.00e+00 | 0e+00 | 1.639405 | 2234 | 1008 | 230 |
| GO:0040011 | locomotion | GO|GO.BP | 0.00e+00 | 0e+00 | 1.491716 | 2234 | 1575 | 327 |
| JAMiller.AIBS.000154 | Highest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 0.00e+00 | 0e+00 | 1.462738 | 2234 | 1729 | 352 |
| GO:0048870 | cell motility | GO|GO.BP | 0.00e+00 | 0e+00 | 1.523905 | 2234 | 1372 | 291 |
| GO:0051674 | localization of cell | GO|GO.BP | 0.00e+00 | 0e+00 | 1.523905 | 2234 | 1372 | 291 |
| GO:0008219 | cell death | GO|GO.BP | 0.00e+00 | 0e+00 | 1.433122 | 2234 | 1865 | 372 |
| JAMiller.AIBS.000193 | CortexWGCNA midfetal M23 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.00e+00 | 0e+00 | 1.619628 | 2234 | 1007 | 227 |
| GO:0071345 | cellular response to cytokine stimulus | GO|GO.BP | 0.00e+00 | 0e+00 | 1.647338 | 2234 | 929 | 213 |
| JAMiller.AIBS.000472 | Genes bound by SOX2 in HUMAN SW620 from PMID 20726797 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.424943 | 2234 | 1911 | 379 |
| GO:0009617 | response to bacterium | GO|GO.BP | 0.00e+00 | 0e+00 | 1.989403 | 2234 | 437 | 121 |
| JAMiller.AIBS.000460 | Genes bound by SMAD2 in human HaCaT from PMID 18955504 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.491200 | 2234 | 1484 | 308 |
| JAMiller.AIBS.000461 | Genes bound by SMAD3 in human HaCaT from PMID 18955504 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.491200 | 2234 | 1484 | 308 |
| GO:0035556 | intracellular signal transduction | GO|GO.BP | 0.00e+00 | 0e+00 | 1.352578 | 2234 | 2571 | 484 |
| JAM:003110 | White Matter | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 2.745810 | 2234 | 157 | 60 |
| GO:0006952 | defense response | GO|GO.BP | 0.00e+00 | 0e+00 | 1.535958 | 2234 | 1263 | 270 |
| GO:0050900 | leukocyte migration | GO|GO.BP | 0.00e+00 | 0e+00 | 2.102391 | 2234 | 352 | 103 |
| GO:0035239 | tube morphogenesis | GO|GO.BP | 0.00e+00 | 0e+00 | 1.729873 | 2234 | 731 | 176 |
| GO:0044421 | extracellular region part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.330628 | 2234 | 2743 | 508 |
| JAMiller.AIBS.000304 | Genes bound by FOXA2 in human HepG2 from PMID 19822575 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.345228 | 2234 | 2553 | 478 |
| GO:0016021 | integral component of membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 1.247971 | 2234 | 4174 | 725 |
| GO:0023057 | negative regulation of signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.529192 | 2234 | 1231 | 262 |
| GO:0005615 | extracellular space | GO|GO.CC | 0.00e+00 | 0e+00 | 1.338796 | 2234 | 2576 | 480 |
| GO:0005576 | extracellular region | GO|GO.CC | 0.00e+00 | 0e+00 | 1.290380 | 2234 | 3274 | 588 |
| JAMiller.AIBS.000289 | Genes bound by ESR1 in MOUSE UTERI from PMID 22446102 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.544062 | 2234 | 1154 | 248 |
| GO:0002443 | leukocyte mediated immunity | GO|GO.BP | 0.00e+00 | 0e+00 | 1.739495 | 2234 | 665 | 161 |
| GO:0010648 | negative regulation of cell communication | GO|GO.BP | 0.00e+00 | 0e+00 | 1.521227 | 2234 | 1228 | 260 |
| GO:0072358 | cardiovascular system development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.759328 | 2234 | 633 | 155 |
| GO:0060429 | epithelium development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.597463 | 2234 | 967 | 215 |
| GO:0072359 | circulatory system development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.601674 | 2234 | 951 | 212 |
| GO:0001944 | vasculature development | GO|GO.BP | 0.00e+00 | 0e+00 | 1.761675 | 2234 | 624 | 153 |
| GO:0002274 | myeloid leukocyte activation | GO|GO.BP | 0.00e+00 | 0e+00 | 1.796218 | 2234 | 576 | 144 |
| GO:0009968 | negative regulation of signal transduction | GO|GO.BP | 0.00e+00 | 0e+00 | 1.542335 | 2234 | 1132 | 243 |
| GO:0002366 | leukocyte activation involved in immune response | GO|GO.BP | 0.00e+00 | 0e+00 | 1.769672 | 2234 | 609 | 150 |
| GO:0006950 | response to stress | GO|GO.BP | 0.00e+00 | 0e+00 | 1.279060 | 2234 | 3376 | 601 |
| GO:0044459 | plasma membrane part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.341137 | 2234 | 2459 | 459 |
| GO:0012501 | programmed cell death | GO|GO.BP | 0.00e+00 | 0e+00 | 1.418878 | 2234 | 1747 | 345 |
| GO:0002684 | positive regulation of immune system process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.618214 | 2234 | 888 | 200 |
| GO:0031224 | intrinsic component of membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 1.235370 | 2234 | 4298 | 739 |
| GO:0051240 | positive regulation of multicellular organismal process | GO|GO.BP | 1.00e-07 | 0e+00 | 1.454081 | 2234 | 1512 | 306 |
| GO:0044425 | membrane part | GO|GO.CC | 1.00e-07 | 0e+00 | 1.195299 | 2234 | 5494 | 914 |
| GO:0002263 | cell activation involved in immune response | GO|GO.BP | 1.00e-07 | 0e+00 | 1.758125 | 2234 | 613 | 150 |
| GO:0006915 | apoptotic process | GO|GO.BP | 1.00e-07 | 0e+00 | 1.425028 | 2234 | 1684 | 334 |
| GO:0048584 | positive regulation of response to stimulus | GO|GO.BP | 1.00e-07 | 0e+00 | 1.378843 | 2234 | 2027 | 389 |
| GO:0048646 | anatomical structure formation involved in morphogenesis | GO|GO.BP | 1.00e-07 | 0e+00 | 1.594071 | 2234 | 933 | 207 |
| GO:0002682 | regulation of immune system process | GO|GO.BP | 1.00e-07 | 0e+00 | 1.494050 | 2234 | 1284 | 267 |
| GO:1902531 | regulation of intracellular signal transduction | GO|GO.BP | 1.00e-07 | 0e+00 | 1.420809 | 2234 | 1689 | 334 |
| GO:0043066 | negative regulation of apoptotic process | GO|GO.BP | 1.00e-07 | 0e+00 | 1.669115 | 2234 | 749 | 174 |
| GO:0009897 | external side of plasma membrane | GO|GO.CC | 1.00e-07 | 0e+00 | 2.248923 | 2234 | 246 | 77 |
| GO:0009887 | animal organ morphogenesis | GO|GO.BP | 1.00e-07 | 0e+00 | 1.603766 | 2234 | 896 | 200 |
| JAMiller.AIBS.000163 | Genes decreasing in fetal and increasing in aging | JA Miller at AIBS|Brain|Age-associated genes|Cortex | 1.00e-07 | 0e+00 | 3.647703 | 2234 | 65 | 33 |
| JAM:003106 | Ventral Thalamus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1.00e-07 | 0e+00 | 2.784833 | 2234 | 129 | 50 |
| GO:0048523 | negative regulation of cellular process | GO|GO.BP | 2.00e-07 | 0e+00 | 1.234359 | 2234 | 4156 | 714 |
| GO:0048514 | blood vessel morphogenesis | GO|GO.BP | 2.00e-07 | 0e+00 | 1.810035 | 2234 | 520 | 131 |
| JAM:003076 | noChangeAD_oligo_plasmaMembrane | JAM|BrainLists|BrainLists.Blalock_AD | 2.00e-07 | 0e+00 | 3.444801 | 2234 | 73 | 35 |
| GO:0001568 | blood vessel development | GO|GO.BP | 2.00e-07 | 0e+00 | 1.747997 | 2234 | 596 | 145 |
| GO:0030855 | epithelial cell differentiation | GO|GO.BP | 2.00e-07 | 0e+00 | 1.813793 | 2234 | 511 | 129 |
| GO:0051094 | positive regulation of developmental process | GO|GO.BP | 3.00e-07 | 0e+00 | 1.500600 | 2234 | 1197 | 250 |
| GO:0043069 | negative regulation of programmed cell death | GO|GO.BP | 3.00e-07 | 0e+00 | 1.645749 | 2234 | 764 | 175 |
| JAMiller.AIBS.000280 | Genes bound by EP300 in MOUSE FORBRAIN MIDBRAIN LIMB HEART from PMID 20729851 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 4.00e-07 | 0e+00 | 1.484949 | 2234 | 1258 | 260 |
| GO:0051241 | negative regulation of multicellular organismal process | GO|GO.BP | 4.00e-07 | 0e+00 | 1.535229 | 2234 | 1053 | 225 |
| GO:0060548 | negative regulation of cell death | GO|GO.BP | 5.00e-07 | 0e+00 | 1.604223 | 2234 | 842 | 188 |
| JAM:002984 | Paravermis_IN_Cerebellar Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 6.00e-07 | 0e+00 | 2.758990 | 2234 | 125 | 48 |
| JAMiller.AIBS.000063 | CortexWGCNA 15-21 post-conception weeks C37 SZ/VZenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 6.00e-07 | 0e+00 | 2.900315 | 2234 | 109 | 44 |
| JAMiller.AIBS.000365 | Genes bound by MYB in MOUSE ERMYB from PMID 21317192 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 7.00e-07 | 0e+00 | 1.630807 | 2234 | 771 | 175 |
| GO:0051093 | negative regulation of developmental process | GO|GO.BP | 8.00e-07 | 0e+00 | 1.603379 | 2234 | 829 | 185 |
| GO:0009607 | response to biotic stimulus | GO|GO.BP | 8.00e-07 | 0e+00 | 1.651923 | 2234 | 722 | 166 |
| JAMiller.AIBS.000112 | HippocampusWGCNA greenyellow SGZenriched astrocyte | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 9.00e-07 | 0e+00 | 2.190509 | 2234 | 246 | 75 |
| GO:0031325 | positive regulation of cellular metabolic process | GO|GO.BP | 1.00e-06 | 0e+00 | 1.289463 | 2234 | 2864 | 514 |
| JAMiller.AIBS.000463 | Genes bound by SMAD4 in HUMAN A2780 from PMID 21799915 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.00e-06 | 0e+00 | 1.350154 | 2234 | 2102 | 395 |
| GO:0001816 | cytokine production | GO|GO.BP | 1.10e-06 | 0e+00 | 1.692536 | 2234 | 641 | 151 |
| JAMiller.AIBS.000110 | HippocampusWGCNA cyan SGZenriched upAge glia/gliogenesis | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 1.40e-06 | 0e+00 | 2.535836 | 2234 | 153 | 54 |
| GO:0008284 | positive regulation of cell proliferation | GO|GO.BP | 1.50e-06 | 0e+00 | 1.627302 | 2234 | 755 | 171 |
| JAMiller.AIBS.000465 | Genes bound by SMAD4 in HUMAN HESC from PMID 21741376 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.60e-06 | 0e+00 | 1.380936 | 2234 | 1795 | 345 |
| GO:0009611 | response to wounding | GO|GO.BP | 1.70e-06 | 0e+00 | 1.730388 | 2234 | 573 | 138 |
| GO:0050794 | regulation of cellular process | GO|GO.BP | 1.90e-06 | 0e+00 | 1.114039 | 2234 | 8984 | 1393 |
| GO:0042060 | wound healing | GO|GO.BP | 2.20e-06 | 0e+00 | 1.807610 | 2234 | 473 | 119 |
| GO:0002237 | response to molecule of bacterial origin | GO|GO.BP | 2.50e-06 | 0e+00 | 2.078480 | 2234 | 280 | 81 |
| GO:0009893 | positive regulation of metabolic process | GO|GO.BP | 2.70e-06 | 0e+00 | 1.268326 | 2234 | 3110 | 549 |
| GO:0042119 | neutrophil activation | GO|GO.BP | 2.80e-06 | 0e+00 | 1.832710 | 2234 | 443 | 113 |
| GO:0036230 | granulocyte activation | GO|GO.BP | 3.10e-06 | 0e+00 | 1.824221 | 2234 | 449 | 114 |
| GO:0006928 | movement of cell or subcellular component | GO|GO.BP | 3.60e-06 | 0e+00 | 1.373109 | 2234 | 1800 | 344 |
| GO:0065007 | biological regulation | GO|GO.BP | 3.60e-06 | 0e+00 | 1.096105 | 2234 | 10147 | 1548 |
| GO:0002520 | immune system development | GO|GO.BP | 4.00e-06 | 0e+00 | 1.577585 | 2234 | 838 | 184 |
| GO:0006959 | humoral immune response | GO|GO.BP | 4.10e-06 | 0e+00 | 2.632624 | 2234 | 131 | 48 |
| GO:0030155 | regulation of cell adhesion | GO|GO.BP | 4.20e-06 | 0e+00 | 1.694770 | 2234 | 602 | 142 |
| JAMiller.AIBS.000491 | Genes bound by STAT3 in MOUSE CD4+T from PMID 20064451 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 4.20e-06 | 0e+00 | 1.597652 | 2234 | 787 | 175 |
| GO:0048534 | hematopoietic or lymphoid organ development | GO|GO.BP | 4.90e-06 | 0e+00 | 1.592616 | 2234 | 794 | 176 |
| GO:0001817 | regulation of cytokine production | GO|GO.BP | 5.30e-06 | 0e+00 | 1.703629 | 2234 | 582 | 138 |
| GO:0033993 | response to lipid | GO|GO.BP | 5.80e-06 | 0e+00 | 1.591585 | 2234 | 790 | 175 |
| JAMiller.AIBS.000176 | CortexWGCNA midfetal M6 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 6.20e-06 | 0e+00 | 1.548739 | 2234 | 900 | 194 |
| GO:0051707 | response to other organism | GO|GO.BP | 6.40e-06 | 0e+00 | 1.637191 | 2234 | 689 | 157 |
| GO:0009719 | response to endogenous stimulus | GO|GO.BP | 6.70e-06 | 0e+00 | 1.418066 | 2234 | 1444 | 285 |
| GO:1901652 | response to peptide | GO|GO.BP | 7.50e-06 | 0e+00 | 1.800165 | 2234 | 455 | 114 |
| GO:0043207 | response to external biotic stimulus | GO|GO.BP | 8.10e-06 | 0e+00 | 1.632452 | 2234 | 691 | 157 |
| JAMiller.AIBS.000477 | Genes bound by SOX9 in MOUSE HFSC from PMID 24532713 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 8.10e-06 | 0e+00 | 1.466053 | 2234 | 1186 | 242 |
| JAMiller.AIBS.000241 | Genes bound by CLOCK in HUMAN 293T from PMID 20551151 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.06e-05 | 1e-07 | 1.865480 | 2234 | 389 | 101 |
| GO:1901700 | response to oxygen-containing compound | GO|GO.BP | 1.22e-05 | 1e-07 | 1.416664 | 2234 | 1415 | 279 |
| GO:0032496 | response to lipopolysaccharide | GO|GO.BP | 1.34e-05 | 1e-07 | 2.056636 | 2234 | 269 | 77 |
| GO:0002444 | myeloid leukocyte mediated immunity | GO|GO.BP | 1.97e-05 | 1e-07 | 1.745313 | 2234 | 494 | 120 |
| GO:0032101 | regulation of response to external stimulus | GO|GO.BP | 2.48e-05 | 1e-07 | 1.617646 | 2234 | 684 | 154 |
| GO:0030667 | secretory granule membrane | GO|GO.CC | 2.79e-05 | 2e-07 | 2.048917 | 2234 | 263 | 75 |
| GO:0071216 | cellular response to biotic stimulus | GO|GO.BP | 3.27e-05 | 2e-07 | 2.245272 | 2234 | 192 | 60 |
| GO:0080134 | regulation of response to stress | GO|GO.BP | 3.55e-05 | 2e-07 | 1.419700 | 2234 | 1331 | 263 |
| GO:2000026 | regulation of multicellular organismal development | GO|GO.BP | 3.64e-05 | 2e-07 | 1.349382 | 2234 | 1821 | 342 |
| GO:0070161 | anchoring junction | GO|GO.CC | 3.72e-05 | 2e-07 | 1.691329 | 2234 | 548 | 129 |
| GO:0030097 | hemopoiesis | GO|GO.BP | 3.81e-05 | 2e-07 | 1.574637 | 2234 | 762 | 167 |
| GO:0012505 | endomembrane system | GO|GO.CC | 3.94e-05 | 2e-07 | 1.212819 | 2234 | 3981 | 672 |
| GO:0043299 | leukocyte degranulation | GO|GO.BP | 4.17e-05 | 2e-07 | 1.743609 | 2234 | 478 | 116 |
| GO:0051246 | regulation of protein metabolic process | GO|GO.BP | 5.57e-05 | 3e-07 | 1.289970 | 2234 | 2434 | 437 |
| GO:0002446 | neutrophil mediated immunity | GO|GO.BP | 6.01e-05 | 3e-07 | 1.767835 | 2234 | 443 | 109 |
| JAM:002841 | Emboliform Nucleus_IN_Cerebellar Nucleus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 6.02e-05 | 3e-07 | 2.411030 | 2234 | 149 | 50 |
| GO:0046903 | secretion | GO|GO.BP | 6.09e-05 | 3e-07 | 1.396779 | 2234 | 1430 | 278 |
| GO:1901653 | cellular response to peptide | GO|GO.BP | 6.17e-05 | 3e-07 | 1.891874 | 2234 | 338 | 89 |
| GO:0071495 | cellular response to endogenous stimulus | GO|GO.BP | 6.42e-05 | 3e-07 | 1.433449 | 2234 | 1223 | 244 |
| JAMiller.AIBS.000506 | Genes bound by SUZ12 in MOUSE MESC from PMID 20075857 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 6.54e-05 | 3e-07 | 1.233382 | 2234 | 3402 | 584 |
| GO:0045595 | regulation of cell differentiation | GO|GO.BP | 6.64e-05 | 3e-07 | 1.368547 | 2234 | 1617 | 308 |
| JAMiller.AIBS.000055 | CortexWGCNA 15-21 post-conception weeks C29 SGenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 6.78e-05 | 3e-07 | 2.056959 | 2234 | 248 | 71 |
| GO:0019221 | cytokine-mediated signaling pathway | GO|GO.BP | 7.41e-05 | 4e-07 | 1.620227 | 2234 | 643 | 145 |
| GO:0071219 | cellular response to molecule of bacterial origin | GO|GO.BP | 7.71e-05 | 4e-07 | 2.309423 | 2234 | 168 | 54 |
| JAM:002881 | Head of Caudate Nucleus_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 7.72e-05 | 4e-07 | 2.525931 | 2234 | 128 | 45 |
| GO:0001525 | angiogenesis | GO|GO.BP | 8.32e-05 | 4e-07 | 1.763559 | 2234 | 440 | 108 |
| GO:0032940 | secretion by cell | GO|GO.BP | 8.50e-05 | 4e-07 | 1.410926 | 2234 | 1324 | 260 |
| GO:0001932 | regulation of protein phosphorylation | GO|GO.BP | 9.39e-05 | 4e-07 | 1.424288 | 2234 | 1246 | 247 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[2]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[2], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[2]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000109 | HippocampusWGCNA brown | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.0000086 | 0.0000000 | 2.818248 | 349 | 767 | 47 |
| JAM:002769 | downAD_mitochondrion | JAM|BrainLists|BrainLists.Blalock_AD | 0.0000105 | 0.0000001 | 4.512364 | 349 | 265 | 26 |
| JAMiller.AIBS.000052 | CortexWGCNA 15-21 post-conception weeks C26 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.0001548 | 0.0000007 | 2.727766 | 349 | 725 | 43 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.0014249 | 0.0000052 | 1.526639 | 349 | 4067 | 135 |
| JAMiller.AIBS.000205 | RegionalWGCNA midfetal M35 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0442692 | 0.0001118 | 5.536002 | 349 | 108 | 13 |
| JAMiller.AIBS.000183 | CortexWGCNA midfetal M13 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0586986 | 0.0001446 | 1.958569 | 349 | 1315 | 56 |
| JAMiller.AIBS.000106 | Genes enriched in the hippocampal SGZ in mouse | JA Miller at AIBS|Brain|Postnatal brain|Markers of cortex layers | 0.3608531 | 0.0007203 | 2.993523 | 349 | 338 | 22 |
| JAMiller.AIBS.000570 | WGCNA Olivedrab2ModuleGenes with enriched ELAVL2 targets | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 1.0000000 | 0.0023508 | 2.480864 | 349 | 482 | 26 |
| JAMiller.AIBS.000228 | Genes bound by BMI1 in MOUSE NPCS from PMID 23680149 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.0000000 | 0.0026512 | 2.177498 | 349 | 697 | 33 |
| JAMiller.AIBS.000349 | Genes bound by KDM5B in MOUSE MESC from PMID 21448134 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.0000000 | 0.0073732 | 1.444917 | 349 | 2992 | 94 |
Save Enrichment Analysis results
save(enrichment, file='./../Data/enrichmentAnalysis.RData')
#load('./../Data/enrichmentAnalysis.RData')Session info
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0
## [2] BrainDiseaseCollection_1.00
## [3] anRichment_1.01-2
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] GenomicFeatures_1.38.2
## [7] GenomicRanges_1.38.0
## [8] GenomeInfoDb_1.22.0
## [9] anRichmentMethods_0.90-1
## [10] WGCNA_1.68
## [11] fastcluster_1.1.25
## [12] dynamicTreeCut_1.63-1
## [13] GO.db_3.10.0
## [14] AnnotationDbi_1.48.0
## [15] IRanges_2.20.2
## [16] S4Vectors_0.24.3
## [17] Biobase_2.46.0
## [18] BiocGenerics_0.32.0
## [19] biomaRt_2.42.0
## [20] knitr_1.24
## [21] doParallel_1.0.15
## [22] iterators_1.0.12
## [23] foreach_1.4.7
## [24] polycor_0.7-10
## [25] expss_0.10.1
## [26] GGally_1.4.0
## [27] gridExtra_2.3
## [28] viridis_0.5.1
## [29] viridisLite_0.3.0
## [30] RColorBrewer_1.1-2
## [31] dendextend_1.13.3
## [32] plotly_4.9.2
## [33] glue_1.3.1
## [34] reshape2_1.4.3
## [35] forcats_0.4.0
## [36] stringr_1.4.0
## [37] dplyr_0.8.3
## [38] purrr_0.3.3
## [39] readr_1.3.1
## [40] tidyr_1.0.2
## [41] tibble_2.1.3
## [42] ggplot2_3.2.1
## [43] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 BiocFileCache_1.10.2
## [5] plyr_1.8.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.0.0
## [9] BiocParallel_1.20.1 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] fansi_0.4.1 magrittr_1.5
## [15] checkmate_1.9.4 memoise_1.1.0
## [17] fit.models_0.5-14 cluster_2.0.8
## [19] annotate_1.64.0 Biostrings_2.54.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] askpass_1.1 prettyunits_1.0.2
## [25] colorspace_1.4-1 blob_1.2.1
## [27] rvest_0.3.5 rappdirs_0.3.1
## [29] rrcov_1.4-7 haven_2.2.0
## [31] xfun_0.8 crayon_1.3.4
## [33] RCurl_1.95-4.12 jsonlite_1.6
## [35] genefilter_1.68.0 impute_1.60.0
## [37] survival_2.44-1.1 gtable_0.3.0
## [39] zlibbioc_1.32.0 XVector_0.26.0
## [41] DelayedArray_0.12.2 DEoptimR_1.0-8
## [43] scales_1.1.0 mvtnorm_1.0-11
## [45] DBI_1.1.0 Rcpp_1.0.3
## [47] xtable_1.8-4 progress_1.2.2
## [49] htmlTable_1.13.1 foreign_0.8-71
## [51] bit_1.1-15.2 preprocessCore_1.48.0
## [53] Formula_1.2-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 ellipsis_0.3.0
## [57] acepack_1.4.1 farver_2.0.3
## [59] pkgconfig_2.0.3 reshape_0.8.8
## [61] XML_3.99-0.3 nnet_7.3-12
## [63] dbplyr_1.4.2 locfit_1.5-9.1
## [65] later_1.0.0 labeling_0.3
## [67] tidyselect_0.2.5 rlang_0.4.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_3.6.0 cli_2.0.1
## [73] generics_0.0.2 RSQLite_2.2.0
## [75] broom_0.5.4 fastmap_1.0.1
## [77] evaluate_0.14 yaml_2.2.0
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] curl_4.3 reprex_0.3.0
## [89] geneplotter_1.64.0 pcaPP_1.9-73
## [91] stringi_1.4.6 highr_0.8
## [93] lattice_0.20-38 Matrix_1.2-17
## [95] vctrs_0.2.2 pillar_1.4.3
## [97] lifecycle_0.1.0 data.table_1.12.8
## [99] bitops_1.0-6 httpuv_1.5.2
## [101] rtracklayer_1.46.0 R6_2.4.1
## [103] latticeExtra_0.6-28 promises_1.1.0
## [105] codetools_0.2-16 MASS_7.3-51.4
## [107] assertthat_0.2.1 SummarizedExperiment_1.16.1
## [109] DESeq2_1.26.0 openssl_1.4.1
## [111] withr_2.1.2 GenomicAlignments_1.22.1
## [113] Rsamtools_2.2.2 GenomeInfoDbData_1.2.2
## [115] hms_0.5.3 grid_3.6.0
## [117] rpart_4.1-15 rmarkdown_1.14
## [119] Cairo_1.5-10 shiny_1.4.0
## [121] lubridate_1.7.4 base64enc_0.1-3